Overview

Dataset statistics

Number of variables16
Number of observations13375
Missing cells39350
Missing cells (%)18.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.9 MiB
Average record size in memory466.3 B

Variable types

Text3
Categorical3
Numeric10

Alerts

Year_of_Release has 211 (1.6%) missing valuesMissing
Critic_Score has 6868 (51.3%) missing valuesMissing
Critic_Count has 6868 (51.3%) missing valuesMissing
User_Score has 7310 (54.7%) missing valuesMissing
User_Count has 7310 (54.7%) missing valuesMissing
Developer has 5310 (39.7%) missing valuesMissing
Rating has 5429 (40.6%) missing valuesMissing
Other_Sales is highly skewed (γ1 = 25.01499252)Skewed
NA_Sales has 3588 (26.8%) zerosZeros
EU_Sales has 4719 (35.3%) zerosZeros
JP_Sales has 8440 (63.1%) zerosZeros
Other_Sales has 5317 (39.8%) zerosZeros

Reproduction

Analysis started2024-06-13 14:32:20.718227
Analysis finished2024-06-13 14:32:25.273656
Duration4.56 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Name
Text

Distinct9757
Distinct (%)73.0%
Missing1
Missing (%)< 0.1%
Memory size1.1 MiB
2024-06-13T16:32:25.419628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length132
Median length89
Mean length23.96897
Min length2

Characters and Unicode

Total characters320561
Distinct characters96
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7593 ?
Unique (%)56.8%

Sample

1st rowGallop & Ride!
2nd rowYu-Gi-Oh! Zexal World Duel Carnival
3rd rowSD Gundam G Generation: Cross Drive
4th rowDragon Ball Z: Ultimate Tenkaichi
5th rowGoosebumps: The Game
ValueCountFrequency (%)
the 2194
 
4.1%
of 1366
 
2.6%
2 935
 
1.8%
613
 
1.2%
no 598
 
1.1%
3 434
 
0.8%
world 310
 
0.6%
game 255
 
0.5%
to 241
 
0.5%
pro 238
 
0.4%
Other values (8436) 45789
86.4%
2024-06-13T16:32:25.636098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
39629
 
12.4%
e 25920
 
8.1%
a 21460
 
6.7%
o 19394
 
6.1%
r 16939
 
5.3%
i 16807
 
5.2%
n 16521
 
5.2%
t 13850
 
4.3%
s 12531
 
3.9%
l 9973
 
3.1%
Other values (86) 127537
39.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 320561
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
39629
 
12.4%
e 25920
 
8.1%
a 21460
 
6.7%
o 19394
 
6.1%
r 16939
 
5.3%
i 16807
 
5.2%
n 16521
 
5.2%
t 13850
 
4.3%
s 12531
 
3.9%
l 9973
 
3.1%
Other values (86) 127537
39.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 320561
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
39629
 
12.4%
e 25920
 
8.1%
a 21460
 
6.7%
o 19394
 
6.1%
r 16939
 
5.3%
i 16807
 
5.2%
n 16521
 
5.2%
t 13850
 
4.3%
s 12531
 
3.9%
l 9973
 
3.1%
Other values (86) 127537
39.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 320561
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
39629
 
12.4%
e 25920
 
8.1%
a 21460
 
6.7%
o 19394
 
6.1%
r 16939
 
5.3%
i 16807
 
5.2%
n 16521
 
5.2%
t 13850
 
4.3%
s 12531
 
3.9%
l 9973
 
3.1%
Other values (86) 127537
39.8%

Platform
Categorical

Distinct31
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size885.1 KiB
DS
1727 
PS2
1710 
PS3
1059 
Wii
1052 
X360
1010 
Other values (26)
6817 

Length

Max length4
Median length3
Mean length2.7671776
Min length2

Characters and Unicode

Total characters37011
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowWii
2nd row3DS
3rd rowDS
4th rowPS3
5th row3DS

Common Values

ValueCountFrequency (%)
DS 1727
12.9%
PS2 1710
12.8%
PS3 1059
 
7.9%
Wii 1052
 
7.9%
X360 1010
 
7.6%
PSP 996
 
7.4%
PS 949
 
7.1%
PC 798
 
6.0%
XB 671
 
5.0%
GBA 658
 
4.9%
Other values (21) 2745
20.5%

Length

2024-06-13T16:32:25.697003image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ds 1727
12.9%
ps2 1710
12.8%
ps3 1059
 
7.9%
wii 1052
 
7.9%
x360 1010
 
7.6%
psp 996
 
7.4%
ps 949
 
7.1%
pc 798
 
6.0%
xb 671
 
5.0%
gba 658
 
4.9%
Other values (21) 2745
20.5%

Most occurring characters

ValueCountFrequency (%)
S 8097
21.9%
P 7169
19.4%
3 2472
 
6.7%
i 2346
 
6.3%
D 2179
 
5.9%
X 1874
 
5.1%
2 1818
 
4.9%
B 1410
 
3.8%
6 1363
 
3.7%
C 1294
 
3.5%
Other values (15) 6989
18.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37011
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 8097
21.9%
P 7169
19.4%
3 2472
 
6.7%
i 2346
 
6.3%
D 2179
 
5.9%
X 1874
 
5.1%
2 1818
 
4.9%
B 1410
 
3.8%
6 1363
 
3.7%
C 1294
 
3.5%
Other values (15) 6989
18.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37011
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 8097
21.9%
P 7169
19.4%
3 2472
 
6.7%
i 2346
 
6.3%
D 2179
 
5.9%
X 1874
 
5.1%
2 1818
 
4.9%
B 1410
 
3.8%
6 1363
 
3.7%
C 1294
 
3.5%
Other values (15) 6989
18.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37011
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 8097
21.9%
P 7169
19.4%
3 2472
 
6.7%
i 2346
 
6.3%
D 2179
 
5.9%
X 1874
 
5.1%
2 1818
 
4.9%
B 1410
 
3.8%
6 1363
 
3.7%
C 1294
 
3.5%
Other values (15) 6989
18.9%

Year_of_Release
Real number (ℝ)

MISSING 

Distinct39
Distinct (%)0.3%
Missing211
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean2006.4929
Minimum1980
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size209.0 KiB
2024-06-13T16:32:25.735166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1980
5-th percentile1996
Q12003
median2007
Q32010
95-th percentile2015
Maximum2020
Range40
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.8702598
Coefficient of variation (CV)0.0029256321
Kurtosis1.841319
Mean2006.4929
Median Absolute Deviation (MAD)4
Skewness-0.99359348
Sum26413472
Variance34.459951
MonotonicityNot monotonic
2024-06-13T16:32:25.774120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
2008 1148
 
8.6%
2009 1132
 
8.5%
2010 1014
 
7.6%
2007 978
 
7.3%
2011 922
 
6.9%
2006 798
 
6.0%
2005 743
 
5.6%
2002 669
 
5.0%
2004 628
 
4.7%
2003 606
 
4.5%
Other values (29) 4526
33.8%
ValueCountFrequency (%)
1980 8
 
0.1%
1981 36
0.3%
1982 30
0.2%
1983 12
 
0.1%
1984 13
 
0.1%
1985 12
 
0.1%
1986 18
0.1%
1987 13
 
0.1%
1988 12
 
0.1%
1989 16
0.1%
ValueCountFrequency (%)
2020 1
 
< 0.1%
2017 3
 
< 0.1%
2016 385
 
2.9%
2015 471
3.5%
2014 480
3.6%
2013 433
 
3.2%
2012 527
3.9%
2011 922
6.9%
2010 1014
7.6%
2009 1132
8.5%

Genre
Categorical

Distinct12
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Memory size942.1 KiB
Action
2667 
Sports
1883 
Misc
1426 
Role-Playing
1194 
Shooter
1065 
Other values (7)
5139 

Length

Max length12
Median length10
Mean length7.1305518
Min length4

Characters and Unicode

Total characters95364
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSports
2nd rowMisc
3rd rowStrategy
4th rowFighting
5th rowAdventure

Common Values

ValueCountFrequency (%)
Action 2667
19.9%
Sports 1883
14.1%
Misc 1426
10.7%
Role-Playing 1194
8.9%
Shooter 1065
 
8.0%
Adventure 1055
 
7.9%
Racing 1006
 
7.5%
Platform 718
 
5.4%
Fighting 688
 
5.1%
Simulation 675
 
5.0%
Other values (2) 997
 
7.5%

Length

2024-06-13T16:32:25.814599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
action 2667
19.9%
sports 1883
14.1%
misc 1426
10.7%
role-playing 1194
8.9%
shooter 1065
 
8.0%
adventure 1055
 
7.9%
racing 1006
 
7.5%
platform 718
 
5.4%
fighting 688
 
5.1%
simulation 675
 
5.0%
Other values (2) 997
 
7.5%

Most occurring characters

ValueCountFrequency (%)
t 9817
 
10.3%
o 9267
 
9.7%
i 9019
 
9.5%
n 7285
 
7.6%
e 5366
 
5.6%
r 5254
 
5.5%
c 5099
 
5.3%
l 4245
 
4.5%
S 4156
 
4.4%
a 4126
 
4.3%
Other values (17) 31730
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 95364
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 9817
 
10.3%
o 9267
 
9.7%
i 9019
 
9.5%
n 7285
 
7.6%
e 5366
 
5.6%
r 5254
 
5.5%
c 5099
 
5.3%
l 4245
 
4.5%
S 4156
 
4.4%
a 4126
 
4.3%
Other values (17) 31730
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 95364
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 9817
 
10.3%
o 9267
 
9.7%
i 9019
 
9.5%
n 7285
 
7.6%
e 5366
 
5.6%
r 5254
 
5.5%
c 5099
 
5.3%
l 4245
 
4.5%
S 4156
 
4.4%
a 4126
 
4.3%
Other values (17) 31730
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 95364
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 9817
 
10.3%
o 9267
 
9.7%
i 9019
 
9.5%
n 7285
 
7.6%
e 5366
 
5.6%
r 5254
 
5.5%
c 5099
 
5.3%
l 4245
 
4.5%
S 4156
 
4.4%
a 4126
 
4.3%
Other values (17) 31730
33.3%
Distinct547
Distinct (%)4.1%
Missing42
Missing (%)0.3%
Memory size1.0 MiB
2024-06-13T16:32:26.016769image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length38
Median length28
Mean length13.595065
Min length3

Characters and Unicode

Total characters181263
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique188 ?
Unique (%)1.4%

Sample

1st rowTHQ
2nd rowKonami Digital Entertainment
3rd rowNamco Bandai Games
4th rowNamco Bandai Games
5th rowWayForward Technologies
ValueCountFrequency (%)
entertainment 1976
 
8.1%
games 1588
 
6.5%
interactive 1286
 
5.3%
arts 1079
 
4.4%
electronic 1078
 
4.4%
activision 808
 
3.3%
ubisoft 764
 
3.1%
bandai 739
 
3.0%
namco 739
 
3.0%
digital 738
 
3.0%
Other values (608) 13482
55.5%
2024-06-13T16:32:26.288644image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 17115
 
9.4%
e 15980
 
8.8%
i 15464
 
8.5%
n 15001
 
8.3%
a 13593
 
7.5%
o 11208
 
6.2%
10946
 
6.0%
r 9654
 
5.3%
s 7423
 
4.1%
m 7262
 
4.0%
Other values (61) 57617
31.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 181263
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 17115
 
9.4%
e 15980
 
8.8%
i 15464
 
8.5%
n 15001
 
8.3%
a 13593
 
7.5%
o 11208
 
6.2%
10946
 
6.0%
r 9654
 
5.3%
s 7423
 
4.1%
m 7262
 
4.0%
Other values (61) 57617
31.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 181263
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 17115
 
9.4%
e 15980
 
8.8%
i 15464
 
8.5%
n 15001
 
8.3%
a 13593
 
7.5%
o 11208
 
6.2%
10946
 
6.0%
r 9654
 
5.3%
s 7423
 
4.1%
m 7262
 
4.0%
Other values (61) 57617
31.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 181263
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 17115
 
9.4%
e 15980
 
8.8%
i 15464
 
8.5%
n 15001
 
8.3%
a 13593
 
7.5%
o 11208
 
6.2%
10946
 
6.0%
r 9654
 
5.3%
s 7423
 
4.1%
m 7262
 
4.0%
Other values (61) 57617
31.8%

NA_Sales
Real number (ℝ)

ZEROS 

Distinct360
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26022804
Minimum0
Maximum29.08
Zeros3588
Zeros (%)26.8%
Negative0
Negative (%)0.0%
Memory size209.0 KiB
2024-06-13T16:32:26.375852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.08
Q30.24
95-th percentile1.06
Maximum29.08
Range29.08
Interquartile range (IQR)0.24

Descriptive statistics

Standard deviation0.75456103
Coefficient of variation (CV)2.8996146
Kurtosis401.77524
Mean0.26022804
Median Absolute Deviation (MAD)0.08
Skewness14.976342
Sum3480.55
Variance0.56936235
MonotonicityNot monotonic
2024-06-13T16:32:26.426075image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3588
26.8%
0.02 485
 
3.6%
0.03 452
 
3.4%
0.04 450
 
3.4%
0.01 441
 
3.3%
0.05 428
 
3.2%
0.07 411
 
3.1%
0.06 396
 
3.0%
0.09 347
 
2.6%
0.08 345
 
2.6%
Other values (350) 6032
45.1%
ValueCountFrequency (%)
0 3588
26.8%
0.01 441
 
3.3%
0.02 485
 
3.6%
0.03 452
 
3.4%
0.04 450
 
3.4%
0.05 428
 
3.2%
0.06 396
 
3.0%
0.07 411
 
3.1%
0.08 345
 
2.6%
0.09 347
 
2.6%
ValueCountFrequency (%)
29.08 1
< 0.1%
26.93 1
< 0.1%
23.2 1
< 0.1%
15.68 1
< 0.1%
13.96 1
< 0.1%
12.78 1
< 0.1%
11.28 1
< 0.1%
11.27 1
< 0.1%
10.83 1
< 0.1%
9.71 1
< 0.1%

EU_Sales
Real number (ℝ)

ZEROS 

Distinct282
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1421129
Minimum0
Maximum12.76
Zeros4719
Zeros (%)35.3%
Negative0
Negative (%)0.0%
Memory size209.0 KiB
2024-06-13T16:32:26.470602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.02
Q30.11
95-th percentile0.61
Maximum12.76
Range12.76
Interquartile range (IQR)0.11

Descriptive statistics

Standard deviation0.44186886
Coefficient of variation (CV)3.1092805
Kurtosis177.25235
Mean0.1421129
Median Absolute Deviation (MAD)0.02
Skewness10.503606
Sum1900.76
Variance0.19524809
MonotonicityNot monotonic
2024-06-13T16:32:26.516931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4719
35.3%
0.01 1215
 
9.1%
0.02 1060
 
7.9%
0.03 711
 
5.3%
0.04 544
 
4.1%
0.05 451
 
3.4%
0.06 324
 
2.4%
0.07 287
 
2.1%
0.08 249
 
1.9%
0.09 209
 
1.6%
Other values (272) 3606
27.0%
ValueCountFrequency (%)
0 4719
35.3%
0.01 1215
 
9.1%
0.02 1060
 
7.9%
0.03 711
 
5.3%
0.04 544
 
4.1%
0.05 451
 
3.4%
0.06 324
 
2.4%
0.07 287
 
2.1%
0.08 249
 
1.9%
0.09 209
 
1.6%
ValueCountFrequency (%)
12.76 1
< 0.1%
10.95 1
< 0.1%
9.18 1
< 0.1%
9.14 1
< 0.1%
9.09 1
< 0.1%
8.89 1
< 0.1%
8.03 1
< 0.1%
7.47 1
< 0.1%
6.31 1
< 0.1%
6.21 1
< 0.1%

JP_Sales
Real number (ℝ)

ZEROS 

Distinct218
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.076176449
Minimum0
Maximum10.22
Zeros8440
Zeros (%)63.1%
Negative0
Negative (%)0.0%
Memory size209.0 KiB
2024-06-13T16:32:26.559504image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.03
95-th percentile0.35
Maximum10.22
Range10.22
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.30814877
Coefficient of variation (CV)4.0451974
Kurtosis219.1641
Mean0.076176449
Median Absolute Deviation (MAD)0
Skewness11.865062
Sum1018.86
Variance0.094955665
MonotonicityNot monotonic
2024-06-13T16:32:26.602761image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8440
63.1%
0.02 617
 
4.6%
0.01 562
 
4.2%
0.03 424
 
3.2%
0.04 300
 
2.2%
0.05 255
 
1.9%
0.06 239
 
1.8%
0.07 186
 
1.4%
0.08 180
 
1.3%
0.1 124
 
0.9%
Other values (208) 2048
 
15.3%
ValueCountFrequency (%)
0 8440
63.1%
0.01 562
 
4.2%
0.02 617
 
4.6%
0.03 424
 
3.2%
0.04 300
 
2.2%
0.05 255
 
1.9%
0.06 239
 
1.8%
0.07 186
 
1.4%
0.08 180
 
1.3%
0.09 120
 
0.9%
ValueCountFrequency (%)
10.22 1
< 0.1%
7.2 1
< 0.1%
6.81 1
< 0.1%
6.5 1
< 0.1%
6.04 1
< 0.1%
5.38 1
< 0.1%
5.33 1
< 0.1%
5.32 1
< 0.1%
4.87 1
< 0.1%
4.39 1
< 0.1%

Other_Sales
Real number (ℝ)

SKEWED  ZEROS 

Distinct140
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.046770093
Minimum0
Maximum10.57
Zeros5317
Zeros (%)39.8%
Negative0
Negative (%)0.0%
Memory size209.0 KiB
2024-06-13T16:32:26.644057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.01
Q30.03
95-th percentile0.19
Maximum10.57
Range10.57
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.18093093
Coefficient of variation (CV)3.8685175
Kurtosis1135.6634
Mean0.046770093
Median Absolute Deviation (MAD)0.01
Skewness25.014993
Sum625.55
Variance0.032736
MonotonicityNot monotonic
2024-06-13T16:32:26.684941image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5317
39.8%
0.01 2738
20.5%
0.02 1278
 
9.6%
0.03 743
 
5.6%
0.04 529
 
4.0%
0.05 385
 
2.9%
0.06 313
 
2.3%
0.07 280
 
2.1%
0.08 194
 
1.5%
0.09 150
 
1.1%
Other values (130) 1448
 
10.8%
ValueCountFrequency (%)
0 5317
39.8%
0.01 2738
20.5%
0.02 1278
 
9.6%
0.03 743
 
5.6%
0.04 529
 
4.0%
0.05 385
 
2.9%
0.06 313
 
2.3%
0.07 280
 
2.1%
0.08 194
 
1.5%
0.09 150
 
1.1%
ValueCountFrequency (%)
10.57 1
< 0.1%
7.53 1
< 0.1%
3.96 1
< 0.1%
3.29 1
< 0.1%
2.93 1
< 0.1%
2.88 1
< 0.1%
2.84 1
< 0.1%
2.74 1
< 0.1%
2.46 1
< 0.1%
2.42 1
< 0.1%

Global_Sales
Real number (ℝ)

Distinct572
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52555439
Minimum0.01
Maximum40.24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size209.0 KiB
2024-06-13T16:32:26.726488image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.02
Q10.06
median0.17
Q30.47
95-th percentile2.03
Maximum40.24
Range40.23
Interquartile range (IQR)0.41

Descriptive statistics

Standard deviation1.4009841
Coefficient of variation (CV)2.6657261
Kurtosis202.07273
Mean0.52555439
Median Absolute Deviation (MAD)0.14
Skewness11.381329
Sum7029.29
Variance1.9627564
MonotonicityNot monotonic
2024-06-13T16:32:26.767299image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02 910
 
6.8%
0.03 694
 
5.2%
0.04 519
 
3.9%
0.05 512
 
3.8%
0.01 503
 
3.8%
0.06 472
 
3.5%
0.07 425
 
3.2%
0.08 394
 
2.9%
0.11 339
 
2.5%
0.09 334
 
2.5%
Other values (562) 8273
61.9%
ValueCountFrequency (%)
0.01 503
3.8%
0.02 910
6.8%
0.03 694
5.2%
0.04 519
3.9%
0.05 512
3.8%
0.06 472
3.5%
0.07 425
3.2%
0.08 394
2.9%
0.09 334
 
2.5%
0.1 322
 
2.4%
ValueCountFrequency (%)
40.24 1
< 0.1%
35.52 1
< 0.1%
31.37 1
< 0.1%
30.26 1
< 0.1%
29.8 1
< 0.1%
28.92 1
< 0.1%
28.31 1
< 0.1%
24.67 1
< 0.1%
23.21 1
< 0.1%
23.1 1
< 0.1%

Critic_Score
Real number (ℝ)

MISSING 

Distinct82
Distinct (%)1.3%
Missing6868
Missing (%)51.3%
Infinite0
Infinite (%)0.0%
Mean68.931151
Minimum13
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size209.0 KiB
2024-06-13T16:32:26.810489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile43
Q160
median71
Q379
95-th percentile89
Maximum98
Range85
Interquartile range (IQR)19

Descriptive statistics

Standard deviation13.9413
Coefficient of variation (CV)0.20224963
Kurtosis0.12694165
Mean68.931151
Median Absolute Deviation (MAD)9
Skewness-0.60512341
Sum448535
Variance194.35985
MonotonicityNot monotonic
2024-06-13T16:32:26.848182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71 204
 
1.5%
75 197
 
1.5%
70 192
 
1.4%
73 192
 
1.4%
74 189
 
1.4%
76 189
 
1.4%
78 187
 
1.4%
72 182
 
1.4%
68 181
 
1.4%
80 177
 
1.3%
Other values (72) 4617
34.5%
(Missing) 6868
51.3%
ValueCountFrequency (%)
13 1
 
< 0.1%
17 1
 
< 0.1%
19 5
< 0.1%
20 3
 
< 0.1%
21 1
 
< 0.1%
22 1
 
< 0.1%
23 3
 
< 0.1%
24 3
 
< 0.1%
25 5
< 0.1%
26 8
0.1%
ValueCountFrequency (%)
98 3
 
< 0.1%
97 11
 
0.1%
96 14
 
0.1%
95 14
 
0.1%
94 28
 
0.2%
93 36
0.3%
92 43
0.3%
91 59
0.4%
90 60
0.4%
89 77
0.6%

Critic_Count
Real number (ℝ)

MISSING 

Distinct104
Distinct (%)1.6%
Missing6868
Missing (%)51.3%
Infinite0
Infinite (%)0.0%
Mean26.409098
Minimum3
Maximum113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size209.0 KiB
2024-06-13T16:32:26.887277image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q112
median21
Q336
95-th percentile65
Maximum113
Range110
Interquartile range (IQR)24

Descriptive statistics

Standard deviation18.997901
Coefficient of variation (CV)0.71936954
Kurtosis1.0122427
Mean26.409098
Median Absolute Deviation (MAD)11
Skewness1.1542595
Sum171844
Variance360.92022
MonotonicityNot monotonic
2024-06-13T16:32:26.925538image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 237
 
1.8%
5 210
 
1.6%
11 206
 
1.5%
9 205
 
1.5%
8 196
 
1.5%
7 194
 
1.5%
6 188
 
1.4%
17 187
 
1.4%
12 185
 
1.4%
16 178
 
1.3%
Other values (94) 4521
33.8%
(Missing) 6868
51.3%
ValueCountFrequency (%)
3 1
 
< 0.1%
4 237
1.8%
5 210
1.6%
6 188
1.4%
7 194
1.5%
8 196
1.5%
9 205
1.5%
10 168
1.3%
11 206
1.5%
12 185
1.4%
ValueCountFrequency (%)
113 1
 
< 0.1%
107 1
 
< 0.1%
106 1
 
< 0.1%
104 1
 
< 0.1%
102 1
 
< 0.1%
101 2
< 0.1%
100 3
< 0.1%
99 1
 
< 0.1%
98 1
 
< 0.1%
97 1
 
< 0.1%

User_Score
Real number (ℝ)

MISSING 

Distinct94
Distinct (%)1.5%
Missing7310
Missing (%)54.7%
Infinite0
Infinite (%)0.0%
Mean7.1190602
Minimum0
Maximum9.7
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size209.0 KiB
2024-06-13T16:32:26.965234image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.1
Q16.4
median7.5
Q38.2
95-th percentile8.9
Maximum9.7
Range9.7
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.5009131
Coefficient of variation (CV)0.21083023
Kurtosis1.6482058
Mean7.1190602
Median Absolute Deviation (MAD)0.8
Skewness-1.2337421
Sum43177.1
Variance2.2527402
MonotonicityNot monotonic
2024-06-13T16:32:27.004444image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.8 261
 
2.0%
8 228
 
1.7%
8.2 222
 
1.7%
8.5 208
 
1.6%
7.9 202
 
1.5%
7.5 196
 
1.5%
7.7 194
 
1.5%
8.1 187
 
1.4%
8.4 186
 
1.4%
8.3 186
 
1.4%
Other values (84) 3995
29.9%
(Missing) 7310
54.7%
ValueCountFrequency (%)
0 1
< 0.1%
0.2 1
< 0.1%
0.5 1
< 0.1%
0.6 2
< 0.1%
0.7 2
< 0.1%
0.9 1
< 0.1%
1 2
< 0.1%
1.1 2
< 0.1%
1.2 2
< 0.1%
1.3 2
< 0.1%
ValueCountFrequency (%)
9.7 1
 
< 0.1%
9.6 2
 
< 0.1%
9.5 6
 
< 0.1%
9.4 10
 
0.1%
9.3 30
 
0.2%
9.2 38
 
0.3%
9.1 66
0.5%
9 99
0.7%
8.9 127
0.9%
8.8 140
1.0%

User_Count
Real number (ℝ)

MISSING 

Distinct771
Distinct (%)12.7%
Missing7310
Missing (%)54.7%
Infinite0
Infinite (%)0.0%
Mean159.20396
Minimum4
Maximum10665
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size209.0 KiB
2024-06-13T16:32:27.043967image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q110
median24
Q381
95-th percentile719.8
Maximum10665
Range10661
Interquartile range (IQR)71

Descriptive statistics

Standard deviation536.13887
Coefficient of variation (CV)3.3676227
Kurtosis115.94094
Mean159.20396
Median Absolute Deviation (MAD)18
Skewness8.9893151
Sum965572
Variance287444.89
MonotonicityNot monotonic
2024-06-13T16:32:27.084989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 280
 
2.1%
6 270
 
2.0%
5 264
 
2.0%
8 229
 
1.7%
7 224
 
1.7%
9 193
 
1.4%
10 153
 
1.1%
11 149
 
1.1%
13 137
 
1.0%
12 136
 
1.0%
Other values (761) 4030
30.1%
(Missing) 7310
54.7%
ValueCountFrequency (%)
4 280
2.1%
5 264
2.0%
6 270
2.0%
7 224
1.7%
8 229
1.7%
9 193
1.4%
10 153
1.1%
11 149
1.1%
12 136
1.0%
13 137
1.0%
ValueCountFrequency (%)
10665 1
< 0.1%
10179 1
< 0.1%
9629 1
< 0.1%
8713 1
< 0.1%
8665 1
< 0.1%
7322 1
< 0.1%
7064 1
< 0.1%
6430 1
< 0.1%
6383 1
< 0.1%
5999 1
< 0.1%

Developer
Text

MISSING 

Distinct1551
Distinct (%)19.2%
Missing5310
Missing (%)39.7%
Memory size824.9 KiB
2024-06-13T16:32:27.230182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length80
Median length47
Mean length13.39628
Min length2

Characters and Unicode

Total characters108041
Distinct characters75
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique642 ?
Unique (%)8.0%

Sample

1st rowSpike
2nd rowWayForward
3rd rowEA Tiburon
4th rowMidway Studios - Newcastle
5th rowToys for Bob
ValueCountFrequency (%)
games 862
 
5.5%
studios 641
 
4.1%
entertainment 521
 
3.3%
ea 511
 
3.2%
software 381
 
2.4%
ubisoft 368
 
2.3%
interactive 289
 
1.8%
sports 192
 
1.2%
inc 152
 
1.0%
canada 145
 
0.9%
Other values (1554) 11749
74.3%
2024-06-13T16:32:27.547737image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 8870
 
8.2%
a 8158
 
7.6%
7746
 
7.2%
t 7610
 
7.0%
i 7488
 
6.9%
o 7254
 
6.7%
n 6293
 
5.8%
r 5417
 
5.0%
s 5341
 
4.9%
m 3505
 
3.2%
Other values (65) 40359
37.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 108041
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 8870
 
8.2%
a 8158
 
7.6%
7746
 
7.2%
t 7610
 
7.0%
i 7488
 
6.9%
o 7254
 
6.7%
n 6293
 
5.8%
r 5417
 
5.0%
s 5341
 
4.9%
m 3505
 
3.2%
Other values (65) 40359
37.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 108041
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 8870
 
8.2%
a 8158
 
7.6%
7746
 
7.2%
t 7610
 
7.0%
i 7488
 
6.9%
o 7254
 
6.7%
n 6293
 
5.8%
r 5417
 
5.0%
s 5341
 
4.9%
m 3505
 
3.2%
Other values (65) 40359
37.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 108041
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 8870
 
8.2%
a 8158
 
7.6%
7746
 
7.2%
t 7610
 
7.0%
i 7488
 
6.9%
o 7254
 
6.7%
n 6293
 
5.8%
r 5417
 
5.0%
s 5341
 
4.9%
m 3505
 
3.2%
Other values (65) 40359
37.4%

Rating
Categorical

MISSING 

Distinct7
Distinct (%)0.1%
Missing5429
Missing (%)40.6%
Memory size854.8 KiB
E
3195 
T
2381 
M
1236 
E10+
1122 
EC
 
7
Other values (2)
 
5

Length

Max length4
Median length1
Mean length1.4253713
Min length1

Characters and Unicode

Total characters11326
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowT
2nd rowE10+
3rd rowE
4th rowT
5th rowE10+

Common Values

ValueCountFrequency (%)
E 3195
23.9%
T 2381
17.8%
M 1236
 
9.2%
E10+ 1122
 
8.4%
EC 7
 
0.1%
RP 3
 
< 0.1%
K-A 2
 
< 0.1%
(Missing) 5429
40.6%

Length

2024-06-13T16:32:27.605603image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-13T16:32:27.645734image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
e 3195
40.2%
t 2381
30.0%
m 1236
 
15.6%
e10 1122
 
14.1%
ec 7
 
0.1%
rp 3
 
< 0.1%
k-a 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E 4324
38.2%
T 2381
21.0%
M 1236
 
10.9%
1 1122
 
9.9%
0 1122
 
9.9%
+ 1122
 
9.9%
C 7
 
0.1%
R 3
 
< 0.1%
P 3
 
< 0.1%
K 2
 
< 0.1%
Other values (2) 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11326
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 4324
38.2%
T 2381
21.0%
M 1236
 
10.9%
1 1122
 
9.9%
0 1122
 
9.9%
+ 1122
 
9.9%
C 7
 
0.1%
R 3
 
< 0.1%
P 3
 
< 0.1%
K 2
 
< 0.1%
Other values (2) 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11326
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 4324
38.2%
T 2381
21.0%
M 1236
 
10.9%
1 1122
 
9.9%
0 1122
 
9.9%
+ 1122
 
9.9%
C 7
 
0.1%
R 3
 
< 0.1%
P 3
 
< 0.1%
K 2
 
< 0.1%
Other values (2) 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11326
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 4324
38.2%
T 2381
21.0%
M 1236
 
10.9%
1 1122
 
9.9%
0 1122
 
9.9%
+ 1122
 
9.9%
C 7
 
0.1%
R 3
 
< 0.1%
P 3
 
< 0.1%
K 2
 
< 0.1%
Other values (2) 4
 
< 0.1%

Interactions

2024-06-13T16:32:24.612326image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:21.018317image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:21.540547image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:21.969443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:22.348266image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:22.761198image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:23.111672image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:23.475346image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:23.916816image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:24.272708image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:24.647538image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:21.108723image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:21.580391image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:22.010323image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:22.387120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:22.800876image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:23.152336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:23.510188image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:23.953817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:24.308211image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:24.683594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:21.180738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:21.668453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:22.047566image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:22.426085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:22.837154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:23.189830image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:23.546669image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:23.994672image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:24.344753image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:24.718322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:21.274806image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:21.705814image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:22.084724image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:22.462156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:22.871735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:23.227174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:23.583773image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:24.033569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:24.379348image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:24.752859image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:21.323192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:21.742450image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:22.121003image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:22.496346image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:22.906179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:23.262479image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:23.619976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:24.068491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:24.413098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:24.786235image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:21.360709image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:21.778931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:22.156901image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:22.530769image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:22.940393image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:23.298423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:23.655740image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:24.102675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:24.446125image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:24.822056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:21.400174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:21.817907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:22.198400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:22.567704image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:22.977232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:23.336658image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:23.694691image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:24.139055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:24.482050image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:24.854833image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:21.434746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:21.855076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:22.235252image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:22.656679image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:23.009840image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:23.371452image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:23.733154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:24.172294image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:24.513698image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:24.888205image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:21.469849image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:21.892725image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:22.275664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:22.691517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:23.042837image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:23.406264image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:23.849565image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:24.205945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:24.546314image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:24.921006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:21.504736image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:21.929699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:22.310704image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:22.725671image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:23.076072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:23.440223image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:23.883582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:24.238952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2024-06-13T16:32:24.578975image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Missing values

2024-06-13T16:32:24.983262image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-13T16:32:25.058520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-06-13T16:32:25.230013image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

NamePlatformYear_of_ReleaseGenrePublisherNA_SalesEU_SalesJP_SalesOther_SalesGlobal_SalesCritic_ScoreCritic_CountUser_ScoreUser_CountDeveloperRating
9162Gallop & Ride!Wii2008.0SportsTHQ0.130.000.000.010.14NaNNaNNaNNaNNaNNaN
8300Yu-Gi-Oh! Zexal World Duel Carnival3DS2013.0MiscKonami Digital Entertainment0.000.080.080.010.17NaNNaNNaNNaNNaNNaN
6518SD Gundam G Generation: Cross DriveDS2007.0StrategyNamco Bandai Games0.000.000.260.000.26NaNNaNNaNNaNNaNNaN
3307Dragon Ball Z: Ultimate TenkaichiPS32011.0FightingNamco Bandai Games0.270.180.090.070.6158.026.05.477.0SpikeT
12933Goosebumps: The Game3DS2015.0AdventureWayForward Technologies0.050.000.000.010.05NaNNaNNaNNaNWayForwardE10+
10050NFL Head Coach 09X3602008.0SportsElectronic Arts0.110.000.000.010.1167.014.08.428.0EA TiburonE
4140Super Puyo Puyo 2SNES1995.0PuzzleCompile0.000.000.470.000.47NaNNaNNaNNaNNaNNaN
2954Need for Speed (2015)XOne2015.0RacingElectronic Arts0.300.330.000.060.69NaNNaNNaNNaNNaNNaN
9787RushPSP2006.0RacingMidway Games0.110.000.000.010.1255.012.0NaNNaNMidway Studios - NewcastleT
7017Madagascar: Escape 2 AfricaPS32008.0ActionActivision0.120.080.000.030.2358.013.06.84.0Toys for BobE10+
NamePlatformYear_of_ReleaseGenrePublisherNA_SalesEU_SalesJP_SalesOther_SalesGlobal_SalesCritic_ScoreCritic_CountUser_ScoreUser_CountDeveloperRating
16023Colin McRae Rally 2005PC2004.0RacingCodemasters0.000.010.000.000.0283.011.07.150.0CodemastersE
11363The Golf ClubPS42014.0SportsUnknown0.050.020.000.010.0866.011.06.367.0HB Studios MultimediaE
14423Sorcery Saga: The Curse of the Great Curry GodPSV2013.0Role-PlayingRising Star Games0.000.000.030.000.0365.017.07.344.0ZerodivT
4426Transformers: Fall of CybertronX3602012.0ActionActivision0.280.120.000.040.4479.052.08.3160.0High Moon StudiosT
6265Tiny Toon Adventures: Plucky's Big AdventurePS2001.0ActionConspiracy Entertainment0.150.100.000.020.27NaNNaNNaNNaNNaNNaN
11284Animal Planet: Emergency VetsDS2009.0SimulationActivision0.080.000.000.010.08NaNNaNNaNNaNSilverBirch StudiosE
11964Grand KingdomPS42015.0Role-PlayingNippon Ichi Software0.030.000.030.010.0778.033.08.163.0Spike ChunsoftT
5390MonopolyPS32008.0MiscElectronic Arts0.250.050.000.040.3454.04.06.713.0Electronic ArtsE
860Skate 3PS32010.0SportsElectronic Arts0.790.880.000.321.9880.046.07.682.0EA Black BoxT
15795Clover no Kuni no Alice: Wonderful Wonder WorldPSP2011.0AdventureQuinrose0.000.000.020.000.02NaNNaNNaNNaNNaNNaN